DataRobot-Research/drdocs huggingface dataset:
---
title: End-to-end time series demand forecasting Workflow
description: Perform large-scale demand forecasting using DataRobot's Python package.
---

# End-to-end time series demand forecasting workflow {: #end-to-end-time-series-demand-forecasting-workflow}

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/end-to-end/End_to_end_demand_forecasting/){ .md-button }

Demand forecasting models have many common challenges: large quantities of SKUs or series to predict, partial history or irregular history for many SKUs,  multiple locations with different local or regional demand patterns, and cold-start prediction requests from the business for new products. The list goes on.

Time series in DataRobot, however, has a diverse range of functionality to help tackle these challenges. For example:

- Automatic feature engineering and creation of lagged variables across multiple data types, as well as training dataset creation.
- Diverse approaches for time series modeling with text data, learning from cross-series interactions and scaling to hundreds or thousands of series.
- Feature generation from an uploaded calendar of events file specific to your business or use case.
- Automatic backtesting controls for regular and irregular time-series.
- Training dataset creation for irregular series via custom aggregations.
- Segmented modeling, hierarchical clustering for multi-series models, multimodal modeling, and ensembling.
- Periodicity and stationarity detection, and automatic feature list creation with various differencing strategies.
- Cold start modeling on series with limited or no history.
- Insights for all of the above.

In this first installment of a three-part series on demand forecasting, this accelerator provides the building blocks for a time-series experimentation and production workflow. This notebook provides a framework to inspect and handle common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation with the tools mentioned above and more.

The dataset consists of 50 series (46 SKUs across 22 stores) over a two year period with varying series history, typical of a business releasing and removing products over time.
---
title: Need help signing in?
description: This article addresses common questions related to signing up or signing in to the DataRobot AI Platform or the DataRobot Community.

---

# Need help signing in?

This article addresses common questions related to signing up or signing in to the DataRobot AI Platform or the DataRobot Community.

## Are you signing in to the correct AI Platform?

Make sure that you are logging in to the appropriate region of the DataRobot AI Platform. You must log in to the application based on the region selected when registering your account—either [app.datarobot.com](https://app.datarobot.com){ target=_blank } or [app.eu.datarobot.com](https://app.eu.datarobot.com){ target=_blank }.

## Do you have the right password?

If you are not sure if you are entering the right password, try a password reset. Note:

* Make sure you have the right URL for your region before attempting a reset. 
* If you are using enforced SSO, the SSO admin will have to do the reset.
* If you did not complete account setup, you will not get a password reset email. 

## Still having trouble?

If these steps haven't worked and you're still having issues, try the following:

* Make sure you are using the latest version of the Chrome browser, which DataRobot recommends for the best user experience.

* Clear your browser cache and cookies and try accessing the domain again.

* Try signing in with the browser in incognito mode. 

* Contact your administrator.

* If you are in an office or public environment, there may be a firewall blocking website access. Try using a different network to access the site.

## What do my login credentials give me access to?


When you sign up for the DataRobot AI Platform, you can also use those credentials in [DataRobot Community](https://community.datarobot.com){ target=_blank } to post and get involved.

To access [DataRobot University](https://learn.datarobot.com){ target=_blank }, visit the site and create a new set of login credentials.

## How can I add two-factor authentication?

Once you are logged in, you can set up two-factor authentication (2FA):

=== "DataRobot Account Portal (Trial)"

    For a trial of the DataRobot AI Platform, you can enable 2FA from [id.datarobot.com/security](https://id.datarobot.com/security){ target=_blank }. For more information about setting up 2FA, see the full [documentation](2fa#set-up-2fa) on the topic. For help, see [2FA troubleshooting](2fa-help).

    ![](images/dr-account-2fa.png)

=== "DataRobot Profile Settings"

    For DataRobot's enterprise offerings (managed AI Platform or Self-Managed AI Platform), you can set up two-factor authentication (2FA) from your profile settings. Click your profile avatar (or the default avatar ![](images/icon-gen-settings.png)) in the upper-right corner of DataRobot, click **Profile**, and then click **Security**. For more information about setting up 2FA, see the full [documentation](2fa) on the topic. For help, see [2FA troubleshooting](2fa-help).

    ![](images/2fa-1.png)

## Get more assistance

If the suggestions above did not answer your question(s), contact your administrator or reach out to support@datarobot.com.
---
title: V9.0 maintenance releases
description: Maintenance releases for the DataRobot Release 9.0 major release.

---

# V9.0 maintenance releases {: #v90-maintenance-releases }

The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform platform. See also the [features introduced in v9.0.0](v9.0/index), which was introduced _March 29, 2023_.


Version |	Release date
------- | ------
[v9.0.1](v9.0.1-aml) | *June 6, 2023*
[v9.0.2](v9.0.2-aml) | *June 9, 2023*
[v9.0.3](v9.0.3-aml) | *June 30, 2023*
[v9.0.4](v9.0.4-aml) | *July 24, 2023*
|  | Element | Description |
|---|---|---|
| ![](images/icon-1.png) | Filter | Allows you to select a specific [class](vai-ref#image-embeddings)*) to display. All classes display by default.|
| ![](images/icon-2.png) | Image display | Displays projections of images in two dimensions to help you visualize similarities between groups of images and to identify outliers.|
| ![](images/icon-3.png) | Target and Actual tooltip | Displays, on hover, the target and the actual value for the image. Use these tooltips to compare images to see whether DataRobot is grouping images as you would expect.|
| ![](images/icon-4.png) | Zoom controls | Enlarges or reduces the image displayed on the canvas. Alternatively, double-click on the image. To move areas of the display into focus, click and drag. |


See the [reference material](vai-ref#image-embeddings) for more information.
---
title: User AI Accelerators
description: Review user-submitted workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows.
---

# User AI Accelerators {: #user-ai-accelerators }

!!! warning
	The code in these GitHub repos for these accelerators is sourced from the DataRobot user community and is not owned or maintained by DataRobot, Inc. You may need to make edits or updates for this code to function properly in your environment.

![](images/globe1.png)


_Want to contribute your own accelerator?_ Visit the [GitHub accelerator repo](https://github.com/datarobot-community/ai-accelerators/tree/main){ target=_blank } to see how it's done.

This page provides quick descriptions of each customer-contributed AI Accelerator&mdash;notebooks with API code building blocks to help reduce the cycle time from hypothesis to insights with DataRobot. For downloadable code and full description, see and clone the notebooks from the DataRobot [GitHub accelerator repo](https://github.com/datarobot-community/ai-accelerators/tree/main){ target=_blank }.
# Version 7.3.1 {: #version-731 }

_December 16, 2021_

The DataRobot v7.3.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:

* [Features introduced in v7.3.0](v7.3.0-aml)

## Issues fixed in v7.3.1 {: #issues-fixed-in-v731 }

The following issues have been fixed since Enterprise release v7.3.0:

### Platform {: #platform }

* ARCH-3374: MongoDB migrations now support an analyze function to estimate collections impacted for each migration, with results shown on the command line to the cluster admin prior to executing the migration.  A stats command was implemented to gather the full database statistics for collection sizes.
* MODEL-7371: Fixes a bug in grid-search that required square-brackets for search.

## DataRobot Response to Log4j Vulnerability & Recommended Next Steps {: #datarobot-response-to-log4j-vulnerability-recommended-next-steps }

On December 10, 2021, DataRobot became aware of a vulnerability in the widely used logging library Log4j (CVE-2021-44228) for Java-based applications, which is impacting enterprise applications and cloud services around the world. In response to this vulnerability, DataRobot immediately assembled a cross-functional team to assess the scope of the vulnerability and begin implementing steps for remediation.

Security is a foundational element of an Enterprise AI Platform. We have completed a thorough investigation and currently have no indication of any successful exploitation of this vulnerability in our Managed Cloud environment. We identified remediations and deployed fixes for all of our customers on DataRobot Managed Cloud as of December 12, 2021 and DataRobot DataPrep as of December 13, 2021.
If you currently do not use DataRobot Scoring Code, MLOps Monitoring Agents, or Portable Prediction Servers (PPS) with MLOps Monitoring enabled, no further action is required by you.

For customers using any of the above features, the Log4j vulnerability may continue to exist in any previously generated artifact. As general guidance, please follow the Apache Security Advisory for Log4j for mitigation. If you need further details, please review the appendix for specific mitigation steps on DataRobot artifacts to help address these risks.

Please do not hesitate to reach out to your account team or email support@datarobot.com if we can assist you in any way.
As always, thank you for including DataRobot as a cornerstone in your AI transformation. We will provide updates on Log4j if we have new information relevant to you. For now, we wish you the happiest of holiday seasons.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: R code examples
description: Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows.

---

# R code examples {: #r-code-examples }

The API user guide includes overviews and workflows for DataRobot's R client that outline complete examples of common data science and machine learning workflows. Be sure to review the [API quickstart guide](api-quickstart/index) before using the notebooks below.

Topic | Describes... |
----- | ------ |
[Advanced feature selection with R](r-select.ipynb) | How to select features by creating aggregated feature impact. |
[Prediction Explanation clustering with R](pe-cluster.ipynb) | The analysis and identification of the clusters present in a DataRobot model's Prediction Explanations using the DataRobot R client. |
[Access blueprints with R](r-leaderboard-nb.ipynb) | How to access blueprints from either the Leaderboard or the Repository.
---
title: Zero-shot text classification for error analysis
description: Use zero-shot text classification with large language models (LLMs), focusing on its application in error analysis of supervised text classification models.

---

# Zero-shot text classification for error analysis {: #zero-shot-text-classification-for-error-analysis }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced-experimentation/zero_shot_LMM/Zero%20Shot%20Text%20Classification%20for%20Error%20Analysis.ipynb){ .md-button }

This AI Accelerator which offers a deep dive into the utilization of zero-shot text classification for error analysis in machine learning models. This educational resource is an invaluable asset for those interested in enhancing their understanding and proficiency in the field of machine learning.

Building on your existing knowledge and experience with the DataRobot automated machine learning platform, this notebook demonstrates the development of a text classification model. From there, turn your focus towards a crucial, yet sometimes challenging aspect of machine learning - error analysis.

Understanding why a supervised machine learning model incorrectly classifies certain examples can be a challenging task. The newly released notebook introduces a novel methodology for identifying and understanding these errors using zero-shot text classification.

In this accelerator, make use of three different zero-shot classification methods: Natural Language Inference (NLI), Embedding, and Conversational AI. The distinct capabilities of each method contribute to a comprehensive and enlightening error analysis process.

Detailed within the notebook is a thorough explanation of the error analysis procedure. So regardless of your proficiency level in machine learning, the content is structured to cater to a wide range of readers. The application of zero-shot text classification to error analysis could be a significant enhancement to your machine learning practice, particularly with DataRobot.
---
title: Customize lift charts
description: Leverage popular Python packages with DataRobot's Python client to recreate and augment DataRobot's lift chart visualization.

---

# Customize lift charts {: #customize-lift-charts }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced-experimentation/customizing_lift_charts){ .md-button }

Ever wanted to plot more than 60 bins in DataRobot's lift chart?

Ever needed to present this graphic with a specific color palette?

Ever required to display more information in the chart due to regulatory reasons?

In this AI Accelerator, leverage popular Python packages with DataRobot's Python client to recreate and augment DataRobot's lift chart visualization. These customizations allow you to:

* Plot more than 60 bins in DataRobot's lift chart.
* Present this lift chart visualizations with a specific color palette.
* Display more information in the chart.

The steps demonstrated in the accompanying notebook are:

1. Connect to DataRobot
2. Create a DataRobot project
3. Run a single blueprint from the repository
4. Obtain predictions and actuals
5. Recreate DataRobot’s lift chart
6. Add customization to the lift chart
---
title: Use self-joins with panel data to improve model accuracy
description: Explore how to implement self-joins in panel data analysis.

---

# Use self-joins with panel data to improve model accuracy {: #use-self-joins-with-panel-data-to-improve-model-accuracy }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/end-to-end/Demand_forecasting_retraining/End_to_end_demand_forecasting_retraining.ipynb){ .md-button }

In this accelerator, explore how to implement self-joins in panel data analysis. Regardless of your industry, if you work with panel data, this guide is tailored to help you accelerate feature engineering and extract valuable insights.

Panel data, with multiple observations for consistent subjects over time, is ubiquitous in various domains. While panel data is often spread across multiple tables, it can also exist in a single dataset with multiple features suitable as panel dimensions. The self-join technique enables automated, time-aware feature engineering with just one dataset, generating hundreds of candidate features of lagged aggregations and statistics. Combining these features within panel dimensions can substantially improve predictive model performance.

The accelerator focuses on predicting airline take-off delays of 30 minutes or more to illustrate the self-join technique. However, this framework applies broadly across verticals and can easily be adapted to your use case. Using a single dataset, join it four times across different features, engineer time-based features from each join, using the AI Catalog for data management.

The accelerator covers data preparation with multiple joins and time horizons, how to mitigate target leakage with multiple feature lists as well as time gaps in time-aware joins.

Panel data analysis unlocks valuable insights into subjects evolving over time, and is often overlooked when there is a singular dataset.
---
title: Notification service
description: How to use the Notification service to integrate flexible, centralized notifications into your organization's processes as webhooks, emails, and Slack messages.

---

# Notification service {: #notification-service }

The Notification service allows you to integrate flexible and centralized notifications into your organization's processes around change and incident management. [Notification channels](web-notify), available as webhooks, emails, and Slack messages, allow users in an organization to subscribe to certain DataRobot events. When a notification event is triggered, an HTTP POST payload is sent to the webhook's configured URL, or an email is sent.

Various DataRobot events, such as sharing projects, deployment activity, or Autopilot completing, generate notifications. When you configure a notification channel, you can choose which events you want to receive notifications for. Each event relates to a unique action within DataRobot. Choose to opt in to all events for a configuration, or subscribe to specific events.


!!! note
    The [notification center](getting-help#notification-center), available to users from the management tools in the upper right, is based on this notification service but is determined by a default system policy and is not configurable.

 These sections describe:

| Topic | Description |
|-------|-------------|
| [Notification channels and policies](web-notify) | Create notification channels and policies. |
| [Webhook event payloads](web-events) | Read about event payload configurations available for DataRobot webhooks. |
| [Mute deployment notifications](web-mute) | Stop receiving notifications for deployment-specific events tied to a configured policy.|
| [Maintenance notifications](maintenance-notes) | Configure notifications that communicate service interruption. |

## Considerations {: #considerations }

Consider the following when using the Notification service:

- Webhook channels do not support the ability to change a webhook payload to send notifications with specifically formatted messages.
- Notification channels do not support Adaptive Card format for webhook messages, which means Microsoft Teams integration isn't currently possible.
---
title: Add prediction data post-deployment
description: How to add historical prediction data after a model is deployed.

---

# Add prediction data post-deployment {: #add-prediction-data-post-deployment }

Users with the [Owner](roles-permissions) role can add historical prediction data to deployments if data drift is enabled. To do so, navigate to the **Settings** tab and select **choose file** under the **Inference** header to upload your prediction data in XLSX, CSV, or TXT format. You can also select prediction data from the **AI Catalog**.

![](images/add-deploy-15.png)

Training data is a critical component for calculating data drift. If you did not include training data when you created a deployment, or if there was an error when uploading that data, add it from the [**Data Drift > Settings**](data-drift-settings) tab. You can also check for training data from the [**Data Drift > Summary**](data-drift) tab.

The data must meet the following requirements:

* Appended prediction data must have the same features as the original prediction dataset. After uploading new data, DataRobot prompts you to confirm the addition because you cannot later remove data from a deployment. To use different prediction data, create a new deployment.

* An uploaded training dataset must include the same features as the prediction (scoring) dataset. You cannot replace training data. If you want a deployment to use different training data, create a new deployment with the appropriate data.
!!! note
    Specified pairwise interactions are not guaranteed to appear in a model's output. Only the interactions that add signal to a model according to the algorithm will be featured in the output. For example, if you specify an interaction group of features A, B, and C, then AxB, BxC, and AxC are the interactions considered during model training. If only AxB adds signal to the model, then only AxB is included in the model's output (excluding BxC and AxC).
---
title: No-Code AI Apps
description: Create and configure AI-powered applications in Workbench using a no-code interface to enable core DataRobot services.

---

# No-Code AI Apps {: #no-code-ai-apps }

{% include 'includes/no-code-app-intro.md' %}

See the associated [considerations](app-builder/index#considerations){ target=_blank } for important additional information, and view the latest [public preview features](wb-app-pp/index) for DataRobot apps.

## Applications tab {: #applications-tab }

The **Applications** tab lists any apps that you or a team member has created in a Use Case.

From this tab, you can:

![](images/wb-app-6.png)

|    | Element  | Description |
|----|----------|-------------|
| <div class="table-label">1</div> | Application name | Click to launch an application. |
| <div class="table-label">2</div> | Source           | View the model used to create the application. |
| <div class="table-label">3</div> | Settings         | Control the columns displayed in this tab. |
| <div class="table-label">4</div> | More options     | Click to remove an application from the Use Case. |

## Create an app {: #create-an-app }

In Workbench, you can create applications directly from a model in your experiment, so that you and other team members can quickly begin making predictions and generating data visualizations.

To create an app:

1. Follow the instructions to [set up and run an experiment](wb-experiment/index). Then, select the model you want to use to power the application.
2. Click **Model actions > Create app**.

    ![](images/wb-app-1.png)

3. Select an [application type](create-app#template-options){ target=_blank }: Predictor, What-if, or Optimizer. Your selection determines the initial configuration of the app.

    ![](images/wb-app-2.png)

    ??? tip "Select a new application type"
        To change application type once you've made a selection, click **Select Application type **at the top of the page.

        ![](images/wb-app-5.png)

4. Name the app and choose a [sharing option](app-settings#permissions)&mdash;_Anyone With the Sharing Link_ automatically generates a link that can be shared with non-DataRobot users while _Invited Users Only_ limits sharing to other DataRobot users, groups, and organizations.

    ![](images/wb-app-3.png)

5. When you're done, click **Build Application**. In a new tab, you'll be prompted to sign in with DataRobot and authorize read/write access.

    ![](images/wb-app-4.png)

## Next steps {: #next-steps }

From here, you can:

- [Configure](edit-apps/index){ target=_blank } and [use](use-apps/index){ target=_blank } the application.
- [Share your application](current-app#share-applications){ target=_blank }